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Supporting the Selection of Quality Tools Using Neural Networks 1

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4th International Conference on Intelligent Systems in Production Engineering and Maintenance , ISPEM 2023

作者： Starzyńska, Beata Rojek, Izabela Faculty of Mechanical Engineering Poznan University of Technology M. Sklodowska-Curie Square 5 Poznan60965 Poland Faculty of Computer Science Kazimierz Wielki University Chodkiewicza 30 Bydgoszcz85064 Poland

ISBN: (数字)9783031450211

ISBN: (纸本)9783031450204

Quality management tools are well-grounded in the management of enterprises regardless of an adopted quality management concept. A crucial problem to be solved is the provision of support for the selection of these tools in such a way as to choose the most useful one. The article presents an overview of traditional solutions for the selection of quality tools and their computer-aided choice. The analysis of the source literature showed a research gap regarding solutions for automatic support for the selection of quality tools. In order to resolve this problem, neural networks were used, specifically a feedforward multilayer network with backward propagation of errors. Data were prepared in the form of learning examples and many classification models based on the selected neural network were developed. As a result, the best model with the highest classification effectiveness was selected. Such a classification model can be placed in an expert system, which can then support a less experienced employee in the selection of quality tools (e.g. in the quality assurance department in an enterprise). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Quality assurance

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Optimal Visualization of Hyper Spectral Time Series Algorithms via Graphical Representations 5th

Optimal Visualization of Hyper Spectral Time Series Algorith...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Trapty Karthikeyan, M.P. Adhikari, Manoj S. Ulla, Mohammed Mujeer Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This technical summary describes how using graphical representations can allow for the best visualization of hyper spectral time series algorithms. Hyper spectral time collection algorithms correctly analyze temporal information from satellite- or plane-borne sensors, for instance, to offer an in-intensity expertise of an environment or system. But, such evaluation can regularly be difficult to realize without graphical aid. This research gives a method for correctly visualizing hyper spectral time series algorithms using graphical representations, consisting of line graphs and tree diagrams. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Visualization

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Label Correlation Guided Feature Selection for Multi-label Learning 9th

Label Correlation Guided Feature Selection for Multi-label ...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Kai Liang, Wei Cao, Peng Yang, Jinzhu Li, Weiping Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China School of Software and Microelectronics Peking University Beijing China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Multi-label learning has received much attention due to its wide range of application domains. Multi-label data often has high-dimensional features, which brings more challenges to classification algorithms. Feature selection based on sparse learning is one of the most important approaches, which can select discriminative features to alleviate the curse of dimensionality. However, most of these methods do not consider feature redundancy and label correlation simultaneously. In this work, we propose a multi-label feature selection method that takes feature redundancy and label correlation into account. Specifically, the proposed method first divides features into groups according to feature correlation and then encourages competition within each group but relaxes competition between groups to eliminate redundant and irrelevant features. Moreover, we propose an approach to capture label correlation and exploit it to improve the feature selection. Finally, an iterative optimization algorithm is designed to obtain the feature weights for multi-label feature selection. Extensive experiments on various multi-label datasets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature Selection

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An Analysis on Cognitive Radio Adhoc Networks: Categories, Problems, and Solutions 1

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14th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022

作者： Vinya, Viyyapu Lokeshwari Sahithi, G. Gopal, S. Venu Rao, G. Venkateswara Shaikh, Mehwish Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Shamshabad India Department of Computer Science and Engineering CVR College of Engineering Telangana Ibrahimpatnam India Department of Artificial Intelligenceand Data Science Vardhaman College of Engineering Telangana Shamshabad India Department of Computer Science and Engineering GIT GITAM-Deemed to be University A.P. Visakhapatnam India Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

The exploitation of finite spectrum resources is being addressed by the new technology known as Cognitive Radio (CR). It has emerged as a potential remedy for the spectrum shortage problem in the following generation of cellular networks by utilizing opportunities in the time, frequency, and spatial domains. Many group-oriented applications that are created for CRAHN deployment have grown significantly in relevance in recent years. Multicasting is a more effective method for enabling applications that rely on group communication. In contrast to traditional wired multicast routing protocols, multicast routing for CRNs must manage a range of challenges, because of the characteristics of CRNs, such as low bandwidth, mobility, and low power. In this paper, we discussed about various types, issues and solutions for CRAHN routing, Multicast Routing, Channel Assignment and Load balancing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multicasting

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Learning a Deep Fourier Attention Generative Adversarial Network for Light Field Image Super-Resolution 12th

Learning a Deep Fourier Attention Generative Adversarial Ne...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Li, Zhipeng Ma, Jian Liang, Dong Xu, Guoming Zhang, Xiaoyin Wang, Junbo School of Internet Anhui University Hefei230039 China School of Computer Science Fudan University Shanghai200433 China

ISBN: (纸本)9783031463167

Human eyes can see the three-dimensional (3D) world because they receive the light emitted by objects, and the light field (LF) is a complete representation of the set of light in the 3D world. Light field image super resolution (LFISR) aims at reconstructing high-resolution LF images from their low-resolution counterparts which captured by the LF camera. In recent years, although convolutional neural networks (CNNs) can bring good performance to LFISR tasks, they cannot recover more real finer texture details. Therefore, it is a difficult challenge to generate realistic images of LF that can satisfy human perception. To address these problems, we propose a novel LFISR model by learning a deep Fourier attention generative adversarial network (GAN). Specifically, the generator network and loss function of the traditional single image super-resolution reconstruction (SISR) GAN model are improved for LFISR reconstruction. Furthermore, we design an attention module based on deep Fourier channel, which leverages the frequency content difference across distinct features to learn precise hierarchical representations of high-frequency information of diverse structures. Extensive experiments are performed on the mainstream LF datasets, leading to the state-of-the-art results of our method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Generative adversarial networks

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Iranian Architectural Styles Recognition Using Image Processing and Deep Learning 6th

Iranian Architectural Styles Recognition Using Image Process...

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6th International Conference on Dynamics of Information Systems, DIS 2023

作者： Darbandy, Mohammad Tayarani Zojaji, Benyamin Sani, Fariba Alizadeh School of Architecture Islamic Azad University Taft Taft*** Iran Sadjad University Mashhad Iran Mashhad University of Medical Science Mashhad Iran

ISBN: (纸本)9783031503191

Iranian architecture, also known as Persian architecture, encompasses the design of buildings in Iran and extends to various regions in West Asia, the Caucasus, and Central Asia. With a rich history dating back at least 5,000 BC, it boasts distinctive features and styles. Iran, located in the Middle East, has faced ongoing geopolitical challenges, including the potential for conflicts, such as those in Iraq and Afghanistan. Unfortunately, historical monuments often become unintentional casualties during wartime, suffering damage or destruction. These historical monuments hold cultural and historical significance not only for the country they belong to but for all of humanity. Therefore, it is crucial to make efforts to preserve them. In this paper, we propose the development of an automated system utilizing Deep Learning methods for the detection and recognition of historical monuments. This system can be integrated into military equipment to help identify the architectural style of a building and determine its construction date. By doing so, it can provide a critical warning to prevent the targeting of historically significant structures. To support our system, we have curated a dataset consisting of approximately 3,000 photographs showcasing six distinct styles of Iranian historical architecture. Figure 1 provides some examples of these photographs. It is worth noting that this dataset can be valuable for various scientific research projects and applications beyond our proposed system. Additionally, it offers tourists the opportunity to learn about Iranian historical monuments independently, using their mobile phones to access information about a monument’s historical period and architectural style, eliminating the need for a traditional guide. This initiative aims to safeguard the invaluable cultural heritage of Iran and neighboring regions, contributing to the collective preservation of these historical treasures. © 2024, The Author(s), under exclusive license t

关键词： Image classification

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GuidingBand: A Precise Tactile Hand Guidance System to Aid Visual Perception 19th

GuidingBand: A Precise Tactile Hand Guidance System to Aid V...

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19th International-Federation-for-Information-Processing-Technical-Committee-13 (IFIP TC13) International Conference on Human-computer Interaction (INTERACT)

作者： Waghwase, Atish Joshi, Anirudha Indian Inst Technol IDC Sch Design Mumbai 400076 Maharashtra India

ISBN: (纸本)9783031422799;9783031422805

computerised guidance systems can help alleviate tedious everyday tasks such as identifying a desired object in a collection of similar objects. Such guidance systems can prove useful as microinteractions if they are made accessible as a consumer wearable that delivers tactile feedback. We designed a wrist-wearable tactile guidance system called GuidingBand that provides vibrational cues to help the user pick visual targets out of an array. We conducted two studies to evaluate it. The studies involve presenting visual targets to users on a screen and giving them visual search tasks. In study 1, we identified the error rate of our guidance system. We presented users (N=20) with arrays of identical, square targets to pick from, progressively reduced the target sizes and evaluated error rates for each size. Notably, we observed a 4% error rate at a target size of 10 mm. In study 2, we compared the error rate of the guidance system with and without the help of human visual perception in a visual search task. We constructed a task that involved showing users an array of rectangles varying only in length and asked them to identify the correct target which was previously shown to them. Users (N=13) had fewer errors when they tried to identify targets with tactile guidance alone, followed by guidance and perception combined and perception alone. It was surprising that instead of improving the precision of the users' performance, their visual perception in fact deteriorated it.

关键词： Vibrotactile Spatial Guidance Precise Hand-tracking Wearable Human-computer Interaction

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Decentralized Vision-Based Byzantine Agent Detection in Multi-robot Systems with IOTA Smart Contracts 15th

Decentralized Vision-Based Byzantine Agent Detection in Mult...

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15th International Symposium on Foundations and Practice of Security (FPS)

作者： Salimpour, Sahar Keramat, Farhad Queralta, Jorge Pena Westerlund, Tomi Univ Turku Turku Intelligent Embedded & Robot Syst Turku Finland

ISBN: (纸本)9783031301216;9783031301223

Multiple opportunities lie at the intersection of multi-robot systems and distributed ledger technologies (DLTs). In this work, we investigate the potential of new DLT solutions such as IOTA, for detecting anomalies and byzantine agents in multi-robot systems in a decentralized manner. Traditional blockchain approaches are not applicable to real-world networked and decentralized robotic systems where connectivity conditions are not ideal. To address this, we leverage recent advances in partition-tolerant and byzantine-tolerant collaborative decision-making processes with IOTA smart contracts. We show how our work in vision-based anomaly and change detection can be applied to detecting byzantine agents within multiple robots operating in the same environment. We show that IOTA smart contracts add a low computational overhead while allowing to build trust within the multi-robot system. The proposed approach effectively enables byzantine robot detection based on the comparison of images submitted by the different robots and detection of anomalies and changes between them.

关键词： Distributed ledger technologies Blockchain Deep learning Anomaly detection Change detection Multi-robot systems computer vision IOTA Smart Contracts Distributed Robotic Systems

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Graph-Based Semi-supervised Learning Using Riemannian Geometry Distance for Motor Imagery Classification 1

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15th Mexican Conference on Pattern Recognition (MCPR)

作者： SmrkovskT, Eric Cecotti, Hubert Calif State Univ Fresno Dept Comp Sci Fresno CA 93740 USA

ISBN: (数字)9783031337833

ISBN: (纸本)9783031337826;9783031337833

A great challenge for brain-computer interface (BCI) systems is their deployment in clinical settings or at home, where a BCI system can be used with limited calibration sessions. BCI should be ideally self-trained and take advantage of unlabeled data. When performing a task, the EEG signals change over time, hence the recorded signals have non-stationary properties. It is necessary to provide machine-learning approaches that can deal with self-training and/or use semi-supervised learning methods for signal classification. A key problem in graph-based semi-supervised learning is determining the characteristics of the affinity matrix that defines the relationships between examples, including the size of the neighborhood of each example. In this paper, we propose two approaches for building the affinity matrix using the distance between examples and the number of neighbors, with a limited number of hyper-parameters, making it easy to reuse. We also compare the Euclidean distance and Riemannian geometry distances to construct the affinity matrix. We assess the classification performance with motor imagery data with two classes from a publicly available dataset of 14 participants. The results show the interest of the proposed semi-supervised approaches with the use of distances to define the neighborhood using Riemannian geometry-based distances with an average accuracy of 73.75%.

关键词： Semi-supervised learning Brain-computer Interface

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Using Remote Workshops to Promote Collaborative Work in the Context of a UX Process Improvement 12th

Using Remote Workshops to Promote Collaborative Work in the ...

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12th International Conference on Design, User Experience, and Usability (DUXU) Held as Part of the 25th International Conference on Human-computer Interaction (HCII)

作者： Moquillaza, Arturo Falconi, Fiorella Aguirre, Joel Lecaros, Adrian Tapia, Alejandro Paz, Freddy Pontificia Univ Catolica Peru Ave Univ 1801 Lima 32 Peru

ISBN: (纸本)9783031356988;9783031356995

Based on the new collaborative work conditions and previous research, the need to establish a process of evaluation of the User Experience in its remote modality was evidenced. In this sense, it was considered to take an approach oriented to improving processes to elaborate a proposal. As part of the proposal, it was identified that it would be necessary to facilitate a workshop with the stakeholders involved in these processes to work collaboratively. As a case study, a virtual workshop was held with the stakeholders (UX research specialists, HCI specialists, UX designers, and Developers) to obtain feedback and pain points of the AS-IS process and proposed improvement opportunities for the TO-BE process. With the participation of 5 specialists, 19 improvements were identified for the tree testing process (8 improvements for the virtual process and 11 improvements for the face-to-face process) and 18 improvements for the face-to-face process of heuristic evaluation. Given their experience in evaluations and user experience design, the participants could prioritize the high-impact improvements for the evaluation processes analyzed. Because of the results achieved, the inputs to propose the TO-BE process, as well as the feedback received during the facilitation of the workshop, and as well at the end, the team can affirm that the whole objectives of the workshop were achieved. Besides, we can affirm that the facilitation of remote workshops allows for obtaining valuable information from key actors. Finally, we affirm as well that remote workshops allow collaboration and knowledge sharing between the participants, despite the limitations of distance, and their non-physical presence.

关键词： Facilitation Collaborative Work Remote Workshop User Experience Process Improvement Human-computer Interaction

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